---
title: "Dashboard Felicidad y Salud en Europa"
output:
flexdashboard::flex_dashboard:
orientation: columns
vertical_layout: fill
source_code: embed
---
```{r setup, include=FALSE}
### SETUP:
# Definir el repositorio CRAN para instalar paquetes
options(repos = c(CRAN = "<https://cloud.r-project.org>"))
# Paquetes necesarios
packages <- c("knitr","flexdashboard", "ggplot2", "dplyr", "ggridges", "viridis", "plotly", "readr", "here", "shiny", "sf", "rnaturalearth")
# Instalar automáticamente si faltan
installed <- packages %in% rownames(installed.packages())
if (any(!installed)) { install.packages(packages[!installed]) }
# Cargar librerías y dataset
lapply(packages, library, character.only = TRUE)
df <- read.csv(here("Data", "ESS11-depurado.csv"))
```
Salud x Felicidad
=======================================================================
Column {data-width=150}
-----------------------------------------------------------------------
### Felicidad por país
```{r fig.height=7, fig.width=7, out.height="100%"}
sf::sf_use_s2(FALSE)
cntry_counts <- df %>%
group_by(cntry) %>%
summarise(n_part = n())
# Descargar mapa mundial
world <- ne_countries(returnclass = "sf") %>%
st_make_valid()
# Unir datos de participantes
map_data <- world %>%
left_join(cntry_counts, by = c("iso_a2_eh" = "cntry"))
# Calcular puntos dentro de cada país para etiquetas
points <- st_point_on_surface(map_data)
# Definir recorte: de Islandia a Israel
bbox <- st_bbox(c(xmin = -25, xmax = 40, ymin = 28, ymax = 85))
map_data_crop <- st_crop(map_data, bbox)
points_crop <- st_crop(points, bbox)
df_happy <- df %>%
group_by(cntry) %>%
summarise(mean_happy = mean(happy, na.rm = TRUE),
n = n())
world_happy <- map_data_crop %>%
left_join(df_happy, by = c("iso_a2_eh" = "cntry"))
ggplot(world_happy) +
geom_sf(aes(fill = mean_happy)) +
scale_fill_viridis_c(option = "plasma", na.value = "grey90") +
labs(fill = "Felicidad media", title = "Felicidad media por país") +
coord_sf(expand = FALSE) +
theme_minimal() +
theme (plot.margin = unit(c(0.1, 0.1, 0.1, 0.1), "cm"))
```
### Salud por país
```{r, fig.height=7, fig.width=7, out.height="100%"}
df_health <- df %>%
group_by(cntry) %>%
summarise(mean_health = mean(health, na.rm = TRUE),
n = n())
world_health <- map_data_crop %>%
left_join(df_health, by = c("iso_a2_eh" = "cntry"))
ggplot(world_health) +
geom_sf(aes(fill = mean_health)) +
scale_fill_viridis_c(option = "plasma", na.value = "grey90") +
labs(fill = "Salud media") +
coord_sf(expand = FALSE) +
theme_minimal() +
theme (plot.margin = unit(c(0.1, 0.1, 0.1, 0.1), "cm"))
```
Column {data-width=200}
-----------------------------------------------------------------------
### Relación salud y felicidad: número de respuestas
```{r, fig.height=8, fig.width=6, out.width="100%", out.height="auto"}
# Tabla de proporciones
df_bubble <- df %>%
count(health, happy) %>%
group_by(health) %>%
mutate(prop = n / sum(n))
# Bubble heatmap con línea de tendencia
ggplot(df_bubble, aes(x = as.numeric(health), y = as.numeric(happy))) +
geom_point(aes(size = n, color = prop), alpha = 0.7) +
geom_smooth(aes(weight = n), method = "loess", color = "red", se = FALSE) +
scale_size(range = c(3, 15)) +
scale_color_gradient(low = "lightblue", high = "darkblue") +
labs(
x = "Salud autopercibida",
y = "Felicidad autopercibida",
size = "N",
color = "Prop",
title = "Salud vs Felicidad (proporciones)"
) +
theme_minimal() +
theme(
legend.position = "bottom",
legend.box = "horizontal",
legend.box.just = "center",
legend.spacing.x = unit(1, "cm"),
legend.text = element_text(size = 6)
) +
guides(
size = guide_legend(nrow = 2, byrow = TRUE),
color = guide_colorbar(barwidth = 5, barheight = 1)
)
```
Column {data-width=200}
-----------------------------------------------------------------------
### Relación salud y felicidad: proporciones de respuesta
```{r, fig.height=8, fig.width=6, out.width="100%", out.height="auto"}
# Calcular proporciones por combinación happy x health
df_heatmap <- df %>%
filter(!is.na(happy) & !is.na(health)) %>%
count(health, happy) %>%
group_by(health) %>%
mutate(prop = n / sum(n))
# Calcular tendencia: media de happy por cada nivel de health
trend <- df %>%
filter(!is.na(happy) & !is.na(health)) %>%
group_by(health) %>%
summarise(mean_happy = mean(happy, na.rm = TRUE))
# Heatmap con tendencia
ggplot(df_heatmap, aes(x = as.numeric(health), y = as.numeric(happy), fill = prop)) +
geom_tile(color = "white", alpha = 0.9) +
scale_fill_gradient(low = "lightblue", high = "darkblue") +
geom_text(aes(label = scales::percent(prop, accuracy = 1)), color = "white", size = 3) +
geom_line(
data = trend,
aes(x = as.numeric(health), y = mean_happy),
color = "red", size = 1,
inherit.aes = FALSE
) +
geom_point(
data = trend,
aes(x = as.numeric(health), y = mean_happy),
color = "red", size = 2,
inherit.aes = FALSE
) +
labs(
x = "Salud autopercibida",
y = "Felicidad autopercibida",
fill = "Proporción",
title = "Mapa de calor: Salud vs Felicidad"
) +
scale_x_continuous(breaks = unique(as.numeric(df$health))) +
scale_y_continuous(breaks = unique(as.numeric(df$happy))) +
theme_minimal() +
theme(legend.position = "bottom")
```
Felicidad en función de otros factores sociodemográficos
=======================================================================
Column {data-width=200}
-----------------------------------------------------------------------
### Felicidad por rangos de edad
```{r, fig.height=6, fig.width=8, out.width="auto", out.height="100%"}
# Filtrar NA en happy y crear rangos de edad
df_ridges <- df %>%
filter(!is.na(happy) & !is.na(agea)) %>%
mutate(age_group = cut(agea, breaks = seq(15, 90, by = 5), include.lowest = TRUE))
# Calcular media de felicidad por grupo de edad para la línea de tendencia
trend1 <- df_ridges %>%
group_by(age_group) %>%
summarise(mean_happy = mean(happy),
mid_age = mean(as.numeric(age_group)))
# Ridge plot con línea de tendencia
ggplot(df_ridges, aes(x = happy, y = age_group, fill = stat(x))) +
geom_density_ridges_gradient(scale = 3, rel_min_height = 0.01) +
geom_point(
data = trend1,
aes(x = mean_happy, y = age_group, color = "Felicidad media"), size = 2) +
geom_smooth(
data = trend1,
aes(x = mean_happy, y = as.numeric(age_group), color = "Felicidad media"),
method = "loess",
se = FALSE,
linewidth = 1) +
scale_fill_viridis_c(option = "plasma", guide = guide_colorbar(direction = "horizontal"), alpha = 0.9) +
scale_x_continuous(breaks = 0:10, limits = c(0, 11)) +
scale_color_manual(values = c("Felicidad media" = "red")) +
labs(
x = "Felicidad",
y = "Rango de edad",
fill = "Felicidad",
color = "") +
theme_minimal() +
ggtitle("Distribución de felicidad por rangos de edad") +
theme(
axis.text.y = element_text(size = 10),
axis.title.y = element_text(size = 12),
axis.text.x = element_text(size = 10),
axis.title.x = element_text(size = 12),
legend.text = element_text(size = 8),
legend.position = "bottom",
legend.box = "horizontal"
)
```
Column {data-width=200}
-----------------------------------------------------------------------
### género x felicidad
```{r, fig.height=6, fig.width=8, out.width="100%", out.height="auto"}
df_prop3 <- df %>%
filter(!is.na(happy)& !is.na(gndr)) %>%
group_by(gndr, happy) %>%
summarise(n = n()) %>%
mutate(prop = n / sum(n))
ggplot(df_prop3, aes(x = factor(gndr), y = prop, fill = factor(happy))) +
geom_bar(stat = "identity", position = "fill") +
scale_y_continuous(labels = scales::percent_format()) +
scale_fill_viridis_d(option = "plasma") +
scale_x_discrete(labels = c("1" = "Hombre", "2" = "Mujer")) +
labs(x = "Género", y = "Proporción", fill = "Nivel de felicidad percibida",
title = "Distribución de felicidad por género") +
theme_minimal()
```
### Felicidad x país de nacimiento
```{r, fig.height=6, fig.width=8, out.width="100%", out.height="auto"}
df_prop1 <- df %>%
filter(!is.na(happy)& !is.na(brncntr)) %>%
group_by(brncntr, happy) %>%
summarise(n = n()) %>%
mutate(prop = n / sum(n))
ggplot(df_prop1, aes(x = factor(brncntr), y = prop, fill = factor(happy))) +
geom_bar(stat = "identity", position = "fill") +
scale_y_continuous(labels = scales::percent_format()) +
scale_fill_viridis_d(option = "plasma", alpha = 0.9) +
labs(x = "Origen", y = "Proporción", fill = "Nivel de felicidad percibida",
title = "Distribución de felicidad por origen") +
theme_minimal()
```
Salud percibida en función de otros factores sociodemográficos
=======================================================================
Column {data-width=200}
-----------------------------------------------------------------------
### Salud x edad
```{r, fig.height=6, fig.width=8, out.width="auto", out.height="100%"}
# Filtrar NA en health y crear rangos de edad
df_ridges2 <- df %>%
filter(!is.na(health) & !is.na(agea)) %>%
mutate(age_group = cut(agea, breaks = seq(15, 90, by = 5), include.lowest = TRUE))
# Calcular media de health por grupo de edad para la línea de tendencia
trend2 <- df_ridges2 %>%
group_by(age_group) %>%
summarise(mean_health = mean(health),
mid_age = mean(as.numeric(age_group)))
ggplot(df_ridges2, aes(x = health, y = age_group, fill = stat(x))) +
geom_density_ridges_gradient(scale = 2, rel_min_height = 0.00001) +
geom_point(
data = trend2,
aes(x = mean_health, y = age_group, color = "Salud media"), size = 2) +
geom_smooth(
data = trend2,
aes(x = mean_health, y = as.numeric(age_group), color = "Salud media"),
method = "loess",
se = FALSE,
linewidth = 1) +
scale_fill_viridis_c(option = "plasma", guide = guide_colorbar(direction = "horizontal")) +
scale_x_continuous(breaks = 0:5, limits = c(0, 5.5)) +
scale_color_manual(values = c("Salud media" = "red")) +
labs(
x = "Salud",
y = "Rango de edad",
fill = "Salud",
color = "") +
theme_minimal() +
ggtitle("Distribución de salud por rangos de edad") +
theme(
axis.text.y = element_text(size = 10),
axis.title.y = element_text(size = 12),
axis.text.x = element_text(size = 10),
axis.title.x = element_text(size = 12),
legend.text = element_text(size = 8),
legend.position = "bottom",
legend.box = "horizontal"
)
```
Column {data-width=200}
-----------------------------------------------------------------------
### Genero x salud
```{r, fig.height=6, fig.width=8, out.width="100%", out.height="auto"}
df_prop4 <- df %>%
filter(!is.na(gndr)& !is.na(health)) %>%
group_by(gndr, health) %>%
summarise(n = n()) %>%
mutate(prop = n / sum(n))
ggplot(df_prop4, aes(x = factor(gndr), y = prop, fill = factor(health))) +
geom_bar(stat = "identity", position = "fill") +
scale_y_continuous(labels = scales::percent_format()) +
scale_fill_viridis_d(option = "plasma") +
scale_x_discrete(labels = c("1" = "Hombre", "2" = "Mujer")) +
labs(x = "Género", y = "Proporción", fill = "Nivel de salud percibida",
title = "Distribución de salud por género") +
theme_minimal()
```
### Salud x país nacimiento
```{r, fig.height=6, fig.width=8, out.width="100%", out.height="auto"}
df_prop2 <- df %>%
filter(!is.na(brncntr)& !is.na(health)) %>%
group_by(brncntr, health) %>%
summarise(n = n()) %>%
mutate(prop = n / sum(n))
ggplot(df_prop2, aes(x = factor(brncntr), y = prop, fill = factor(health))) +
geom_bar(stat = "identity", position = "fill") +
scale_y_continuous(labels = scales::percent_format()) +
scale_fill_viridis_d(option = "plasma") +
labs(x = "Origen", y = "Proporción", fill = "Nivel de salud percibida",
title = "Distribución de salud por origen") +
theme_minimal()
```
